Overview

Dataset statistics

Number of variables47
Number of observations39786
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 MiB
Average record size in memory362.3 B

Variable types

Numeric25
Categorical20
Boolean2

Warnings

pymnt_plan has constant value "False" Constant
initial_list_status has constant value "False" Constant
collections_12_mths_ex_med has constant value "0.0" Constant
application_type has constant value "INDIVIDUAL" Constant
chargeoff_within_12_mths has constant value "0.0" Constant
delinq_amnt has constant value "0" Constant
tax_liens has constant value "0.0" Constant
issue_d has a high cardinality: 55 distinct values High cardinality
zip_code has a high cardinality: 823 distinct values High cardinality
earliest_cr_line has a high cardinality: 526 distinct values High cardinality
last_pymnt_d has a high cardinality: 109 distinct values High cardinality
last_credit_pull_d has a high cardinality: 114 distinct values High cardinality
id is highly correlated with member_idHigh correlation
member_id is highly correlated with idHigh correlation
loan_amnt is highly correlated with funded_amnt and 6 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
int_rate is highly correlated with total_rec_intHigh correlation
installment is highly correlated with loan_amnt and 6 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
total_acc is highly correlated with open_accHigh correlation
out_prncp is highly correlated with out_prncp_invHigh correlation
out_prncp_inv is highly correlated with out_prncpHigh correlation
total_pymnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_pymnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_rec_prncp is highly correlated with loan_amnt and 7 other fieldsHigh correlation
total_rec_int is highly correlated with loan_amnt and 7 other fieldsHigh correlation
recoveries is highly correlated with collection_recovery_feeHigh correlation
collection_recovery_fee is highly correlated with recoveriesHigh correlation
last_pymnt_amnt is highly correlated with total_rec_prncpHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
id is highly correlated with member_idHigh correlation
member_id is highly correlated with idHigh correlation
loan_amnt is highly correlated with funded_amnt and 6 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
int_rate is highly correlated with total_rec_intHigh correlation
installment is highly correlated with loan_amnt and 6 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
total_acc is highly correlated with open_accHigh correlation
out_prncp is highly correlated with out_prncp_invHigh correlation
out_prncp_inv is highly correlated with out_prncpHigh correlation
total_pymnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_pymnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_rec_prncp is highly correlated with loan_amnt and 7 other fieldsHigh correlation
total_rec_int is highly correlated with loan_amnt and 7 other fieldsHigh correlation
recoveries is highly correlated with collection_recovery_feeHigh correlation
collection_recovery_fee is highly correlated with recoveriesHigh correlation
last_pymnt_amnt is highly correlated with total_rec_prncpHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
id is highly correlated with member_idHigh correlation
member_id is highly correlated with idHigh correlation
loan_amnt is highly correlated with funded_amnt and 6 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 6 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
total_acc is highly correlated with open_accHigh correlation
out_prncp is highly correlated with out_prncp_invHigh correlation
out_prncp_inv is highly correlated with out_prncpHigh correlation
total_pymnt is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_pymnt_inv is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_rec_prncp is highly correlated with loan_amnt and 6 other fieldsHigh correlation
total_rec_int is highly correlated with loan_amnt and 6 other fieldsHigh correlation
recoveries is highly correlated with collection_recovery_feeHigh correlation
collection_recovery_fee is highly correlated with recoveriesHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
int_rate is highly correlated with sub_grade and 3 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
sub_grade is highly correlated with int_rate and 3 other fieldsHigh correlation
collection_recovery_fee is highly correlated with recoveriesHigh correlation
installment is highly correlated with funded_amnt_inv and 6 other fieldsHigh correlation
funded_amnt_inv is highly correlated with installment and 7 other fieldsHigh correlation
member_id is highly correlated with issue_d and 2 other fieldsHigh correlation
total_rec_int is highly correlated with int_rate and 9 other fieldsHigh correlation
total_rec_prncp is highly correlated with installment and 7 other fieldsHigh correlation
grade is highly correlated with int_rate and 1 other fieldsHigh correlation
loan_status is highly correlated with out_prncp_inv and 1 other fieldsHigh correlation
total_acc is highly correlated with open_accHigh correlation
issue_d is highly correlated with member_id and 3 other fieldsHigh correlation
pub_rec_bankruptcies is highly correlated with member_id and 3 other fieldsHigh correlation
last_pymnt_amnt is highly correlated with funded_amnt_inv and 5 other fieldsHigh correlation
out_prncp_inv is highly correlated with loan_status and 1 other fieldsHigh correlation
id is highly correlated with member_id and 2 other fieldsHigh correlation
out_prncp is highly correlated with loan_status and 1 other fieldsHigh correlation
recoveries is highly correlated with collection_recovery_feeHigh correlation
verification_status is highly correlated with issue_dHigh correlation
total_pymnt is highly correlated with installment and 7 other fieldsHigh correlation
loan_amnt is highly correlated with installment and 7 other fieldsHigh correlation
funded_amnt is highly correlated with installment and 7 other fieldsHigh correlation
term is highly correlated with int_rate and 2 other fieldsHigh correlation
total_pymnt_inv is highly correlated with installment and 7 other fieldsHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
issue_d is highly correlated with pub_rec_bankruptcies and 7 other fieldsHigh correlation
emp_length is highly correlated with pymnt_plan and 6 other fieldsHigh correlation
pub_rec_bankruptcies is highly correlated with issue_d and 8 other fieldsHigh correlation
pymnt_plan is highly correlated with issue_d and 16 other fieldsHigh correlation
delinq_amnt is highly correlated with issue_d and 16 other fieldsHigh correlation
term is highly correlated with pymnt_plan and 6 other fieldsHigh correlation
application_type is highly correlated with issue_d and 16 other fieldsHigh correlation
tax_liens is highly correlated with issue_d and 16 other fieldsHigh correlation
sub_grade is highly correlated with pymnt_plan and 7 other fieldsHigh correlation
addr_state is highly correlated with pymnt_plan and 6 other fieldsHigh correlation
pub_rec is highly correlated with pub_rec_bankruptcies and 7 other fieldsHigh correlation
initial_list_status is highly correlated with issue_d and 16 other fieldsHigh correlation
grade is highly correlated with pymnt_plan and 7 other fieldsHigh correlation
verification_status is highly correlated with pymnt_plan and 6 other fieldsHigh correlation
home_ownership is highly correlated with pymnt_plan and 6 other fieldsHigh correlation
loan_status is highly correlated with pymnt_plan and 6 other fieldsHigh correlation
chargeoff_within_12_mths is highly correlated with issue_d and 16 other fieldsHigh correlation
collections_12_mths_ex_med is highly correlated with issue_d and 16 other fieldsHigh correlation
annual_inc is highly skewed (γ1 = 30.94133112) Skewed
out_prncp is highly skewed (γ1 = 87.30770026) Skewed
out_prncp_inv is highly skewed (γ1 = 87.43436316) Skewed
collection_recovery_fee is highly skewed (γ1 = 24.82250475) Skewed
id has unique values Unique
member_id has unique values Unique
delinq_2yrs has 35466 (89.1%) zeros Zeros
inq_last_6mths has 19337 (48.6%) zeros Zeros
revol_bal has 996 (2.5%) zeros Zeros
revol_util has 980 (2.5%) zeros Zeros
out_prncp has 39770 (> 99.9%) zeros Zeros
out_prncp_inv has 39770 (> 99.9%) zeros Zeros
total_rec_late_fee has 37708 (94.8%) zeros Zeros
recoveries has 34186 (85.9%) zeros Zeros
collection_recovery_fee has 35958 (90.4%) zeros Zeros

Reproduction

Analysis started2021-07-27 11:41:34.599703
Analysis finished2021-07-27 11:43:03.696535
Duration1 minute and 29.1 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct39786
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683393.9487
Minimum54734
Maximum1077501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:03.792640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum54734
5-th percentile372506.25
Q1516351.75
median666229.5
Q3837871
95-th percentile1039974.75
Maximum1077501
Range1022767
Interquartile range (IQR)321519.25

Descriptive statistics

Standard deviation210676.9987
Coefficient of variation (CV)0.3082804569
Kurtosis-0.7299773136
Mean683393.9487
Median Absolute Deviation (MAD)159954
Skewness0.07721181992
Sum2.718951164 × 1010
Variance4.43847978 × 1010
MonotonicityNot monotonic
2021-07-27T08:43:03.925855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4648991
 
< 0.1%
8649791
 
< 0.1%
7850981
 
< 0.1%
9796591
 
< 0.1%
9673731
 
< 0.1%
8424461
 
< 0.1%
6437911
 
< 0.1%
6007841
 
< 0.1%
5163131
 
< 0.1%
5751751
 
< 0.1%
Other values (39776)39776
> 99.9%
ValueCountFrequency (%)
547341
< 0.1%
557421
< 0.1%
572451
< 0.1%
574161
< 0.1%
589151
< 0.1%
590061
< 0.1%
613901
< 0.1%
614191
< 0.1%
621021
< 0.1%
654261
< 0.1%
ValueCountFrequency (%)
10775011
< 0.1%
10774301
< 0.1%
10771751
< 0.1%
10768631
< 0.1%
10753581
< 0.1%
10752691
< 0.1%
10720531
< 0.1%
10717951
< 0.1%
10715701
< 0.1%
10700781
< 0.1%

member_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct39786
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850793.6757
Minimum70699
Maximum1314167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:04.055216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum70699
5-th percentile388342.75
Q1667054.75
median851544
Q31047527.75
95-th percentile1269507.5
Maximum1314167
Range1243468
Interquartile range (IQR)380473

Descriptive statistics

Standard deviation265636.9839
Coefficient of variation (CV)0.3122225652
Kurtosis-0.5619363928
Mean850793.6757
Median Absolute Deviation (MAD)190275.5
Skewness-0.2139978599
Sum3.384967718 × 1010
Variance7.05630072 × 1010
MonotonicityNot monotonic
2021-07-27T08:43:04.197104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3996491
 
< 0.1%
12254111
 
< 0.1%
3495031
 
< 0.1%
3512281
 
< 0.1%
12643161
 
< 0.1%
8035171
 
< 0.1%
8751981
 
< 0.1%
10697591
 
< 0.1%
6970251
 
< 0.1%
12786611
 
< 0.1%
Other values (39776)39776
> 99.9%
ValueCountFrequency (%)
706991
< 0.1%
736731
< 0.1%
747241
< 0.1%
765831
< 0.1%
803531
< 0.1%
803641
< 0.1%
849141
< 0.1%
854831
< 0.1%
869991
< 0.1%
892431
< 0.1%
ValueCountFrequency (%)
13141671
< 0.1%
13135241
< 0.1%
13117481
< 0.1%
13114411
< 0.1%
13069571
< 0.1%
13067211
< 0.1%
13052011
< 0.1%
13050081
< 0.1%
13049561
< 0.1%
13048841
< 0.1%

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct885
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11231.36028
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:04.333777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15500
median10000
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation7464.542832
Coefficient of variation (CV)0.664616097
Kurtosis0.7631363906
Mean11231.36028
Median Absolute Deviation (MAD)5000
Skewness1.057864295
Sum446850900
Variance55719399.69
MonotonicityNot monotonic
2021-07-27T08:43:04.457694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002835
 
7.1%
120002340
 
5.9%
50002051
 
5.2%
60001908
 
4.8%
150001898
 
4.8%
200001634
 
4.1%
80001589
 
4.0%
250001395
 
3.5%
40001130
 
2.8%
30001031
 
2.6%
Other values (875)21975
55.2%
ValueCountFrequency (%)
5005
 
< 0.1%
7001
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8001
 
< 0.1%
9002
 
< 0.1%
9501
 
< 0.1%
1000301
0.8%
10504
 
< 0.1%
10751
 
< 0.1%
ValueCountFrequency (%)
35000685
1.7%
348002
 
< 0.1%
346751
 
< 0.1%
345251
 
< 0.1%
344755
 
< 0.1%
342001
 
< 0.1%
3400015
 
< 0.1%
339509
 
< 0.1%
336006
 
< 0.1%
335002
 
< 0.1%

funded_amnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1042
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10958.72229
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:04.587117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15400
median9650
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation7194.076908
Coefficient of variation (CV)0.6564704094
Kurtosis0.9323844052
Mean10958.72229
Median Absolute Deviation (MAD)4650
Skewness1.080266385
Sum436003725
Variance51754742.55
MonotonicityNot monotonic
2021-07-27T08:43:04.709872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002743
 
6.9%
120002250
 
5.7%
50002040
 
5.1%
60001898
 
4.8%
150001787
 
4.5%
80001576
 
4.0%
200001463
 
3.7%
250001138
 
2.9%
40001127
 
2.8%
30001023
 
2.6%
Other values (1032)22741
57.2%
ValueCountFrequency (%)
5005
 
< 0.1%
7001
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8001
 
< 0.1%
9002
 
< 0.1%
9501
 
< 0.1%
1000302
0.8%
10505
 
< 0.1%
10751
 
< 0.1%
ValueCountFrequency (%)
35000559
1.4%
348001
 
< 0.1%
346752
 
< 0.1%
345251
 
< 0.1%
344754
 
< 0.1%
342501
 
< 0.1%
3400014
 
< 0.1%
339506
 
< 0.1%
336006
 
< 0.1%
335001
 
< 0.1%

funded_amnt_inv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8215
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10409.01868
Minimum0
Maximum35000
Zeros129
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:04.837593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1875
Q15000
median8975
Q314400
95-th percentile24750
Maximum35000
Range35000
Interquartile range (IQR)9400

Descriptive statistics

Standard deviation7135.760122
Coefficient of variation (CV)0.6855362971
Kurtosis1.055683004
Mean10409.01868
Median Absolute Deviation (MAD)4200
Skewness1.104554929
Sum414133217.2
Variance50919072.52
MonotonicityNot monotonic
2021-07-27T08:43:04.957728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50001309
 
3.3%
100001275
 
3.2%
60001200
 
3.0%
120001072
 
2.7%
8000902
 
2.3%
4000812
 
2.0%
3000804
 
2.0%
15000658
 
1.7%
7000600
 
1.5%
2000453
 
1.1%
Other values (8205)30701
77.2%
ValueCountFrequency (%)
0129
0.3%
0.0001210981
 
< 0.1%
0.0005311331
 
< 0.1%
0.0006546071
 
< 0.1%
0.0018676961
 
< 0.1%
0.0019630931
 
< 0.1%
0.0019669741
 
< 0.1%
0.0022517381
 
< 0.1%
0.0022835981
 
< 0.1%
0.0023730581
 
< 0.1%
ValueCountFrequency (%)
35000135
0.3%
34997.352451
 
< 0.1%
34993.655391
 
< 0.1%
34993.325711
 
< 0.1%
34993.263061
 
< 0.1%
34993.196961
 
< 0.1%
34990.43081
 
< 0.1%
34987.984521
 
< 0.1%
34987.271011
 
< 0.1%
34977.346741
 
< 0.1%

term
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
36 months
29096 
60 months
10690 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters397860
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 60 months
3rd row 36 months
4th row 36 months
5th row 60 months

Common Values

ValueCountFrequency (%)
36 months29096
73.1%
60 months10690
 
26.9%

Length

2021-07-27T08:43:05.179812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:05.246275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
months39786
50.0%
3629096
36.6%
6010690
 
13.4%

Most occurring characters

ValueCountFrequency (%)
79572
20.0%
639786
10.0%
m39786
10.0%
o39786
10.0%
n39786
10.0%
t39786
10.0%
h39786
10.0%
s39786
10.0%
329096
 
7.3%
010690
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter238716
60.0%
Space Separator79572
 
20.0%
Decimal Number79572
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m39786
16.7%
o39786
16.7%
n39786
16.7%
t39786
16.7%
h39786
16.7%
s39786
16.7%
Decimal Number
ValueCountFrequency (%)
639786
50.0%
329096
36.6%
010690
 
13.4%
Space Separator
ValueCountFrequency (%)
79572
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin238716
60.0%
Common159144
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m39786
16.7%
o39786
16.7%
n39786
16.7%
t39786
16.7%
h39786
16.7%
s39786
16.7%
Common
ValueCountFrequency (%)
79572
50.0%
639786
25.0%
329096
 
18.3%
010690
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII397860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79572
20.0%
639786
10.0%
m39786
10.0%
o39786
10.0%
n39786
10.0%
t39786
10.0%
h39786
10.0%
s39786
10.0%
329096
 
7.3%
010690
 
2.7%

int_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct371
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.02787337
Minimum5.42
Maximum24.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:05.327913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.17
Q19.25
median11.86
Q314.59
95-th percentile18.62
Maximum24.59
Range19.17
Interquartile range (IQR)5.34

Descriptive statistics

Standard deviation3.72746646
Coefficient of variation (CV)0.3099023696
Kurtosis-0.4490431486
Mean12.02787337
Median Absolute Deviation (MAD)2.68
Skewness0.2929699244
Sum478540.97
Variance13.89400621
MonotonicityNot monotonic
2021-07-27T08:43:05.456189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.99958
 
2.4%
13.49831
 
2.1%
11.49826
 
2.1%
7.51787
 
2.0%
7.88725
 
1.8%
7.49656
 
1.6%
11.71609
 
1.5%
9.99603
 
1.5%
7.9582
 
1.5%
5.42573
 
1.4%
Other values (361)32636
82.0%
ValueCountFrequency (%)
5.42573
1.4%
5.79410
1.0%
5.99347
0.9%
618
 
< 0.1%
6.03447
1.1%
6.17252
0.6%
6.3958
 
0.1%
6.54305
0.8%
6.62396
1.0%
6.76168
 
0.4%
ValueCountFrequency (%)
24.591
 
< 0.1%
24.41
 
< 0.1%
24.113
 
< 0.1%
23.9111
< 0.1%
23.594
 
< 0.1%
23.529
< 0.1%
23.229
< 0.1%
23.139
< 0.1%
22.942
 
< 0.1%
22.858
< 0.1%

installment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15405
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.7336375
Minimum15.69
Maximum1305.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:06.220375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile71.235
Q1167.08
median280.61
Q3430.78
95-th percentile763.7525
Maximum1305.19
Range1289.5
Interquartile range (IQR)263.7

Descriptive statistics

Standard deviation208.9232116
Coefficient of variation (CV)0.6433679407
Kurtosis1.241388845
Mean324.7336375
Median Absolute Deviation (MAD)123.41
Skewness1.126844554
Sum12919852.5
Variance43648.90835
MonotonicityNot monotonic
2021-07-27T08:43:06.347486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.1168
 
0.2%
180.9659
 
0.1%
311.0254
 
0.1%
150.848
 
0.1%
368.4546
 
0.1%
372.1245
 
0.1%
330.7643
 
0.1%
339.3142
 
0.1%
301.641
 
0.1%
317.7241
 
0.1%
Other values (15395)39299
98.8%
ValueCountFrequency (%)
15.691
< 0.1%
16.081
< 0.1%
16.251
< 0.1%
16.311
< 0.1%
16.471
< 0.1%
19.871
< 0.1%
20.221
< 0.1%
21.251
< 0.1%
21.741
< 0.1%
21.811
< 0.1%
ValueCountFrequency (%)
1305.191
 
< 0.1%
1302.691
 
< 0.1%
1295.211
 
< 0.1%
1288.12
 
< 0.1%
1283.51
 
< 0.1%
1276.63
< 0.1%
1272.21
 
< 0.1%
1269.735
< 0.1%
1265.161
 
< 0.1%
1263.231
 
< 0.1%

grade
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
B
12035 
A
10085 
C
8111 
D
5325 
E
2858 
Other values (2)
1372 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39786
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowB

Common Values

ValueCountFrequency (%)
B12035
30.2%
A10085
25.3%
C8111
20.4%
D5325
13.4%
E2858
 
7.2%
F1054
 
2.6%
G318
 
0.8%

Length

2021-07-27T08:43:06.573154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:06.645981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
b12035
30.2%
a10085
25.3%
c8111
20.4%
d5325
13.4%
e2858
 
7.2%
f1054
 
2.6%
g318
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B12035
30.2%
A10085
25.3%
C8111
20.4%
D5325
13.4%
E2858
 
7.2%
F1054
 
2.6%
G318
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter39786
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B12035
30.2%
A10085
25.3%
C8111
20.4%
D5325
13.4%
E2858
 
7.2%
F1054
 
2.6%
G318
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin39786
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B12035
30.2%
A10085
25.3%
C8111
20.4%
D5325
13.4%
E2858
 
7.2%
F1054
 
2.6%
G318
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII39786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B12035
30.2%
A10085
25.3%
C8111
20.4%
D5325
13.4%
E2858
 
7.2%
F1054
 
2.6%
G318
 
0.8%

sub_grade
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
B3
2924 
A4
2886 
A5
2742 
B5
2709 
B4
 
2514
Other values (30)
26011 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79572
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowC4
3rd rowC5
4th rowC1
5th rowB5

Common Values

ValueCountFrequency (%)
B32924
 
7.3%
A42886
 
7.3%
A52742
 
6.9%
B52709
 
6.8%
B42514
 
6.3%
C12142
 
5.4%
B22058
 
5.2%
C22014
 
5.1%
B11830
 
4.6%
A31810
 
4.5%
Other values (25)16157
40.6%

Length

2021-07-27T08:43:06.879173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b32924
 
7.3%
a42886
 
7.3%
a52742
 
6.9%
b52709
 
6.8%
b42514
 
6.3%
c12142
 
5.4%
b22058
 
5.2%
c22014
 
5.1%
b11830
 
4.6%
a31810
 
4.5%
Other values (25)16157
40.6%

Most occurring characters

ValueCountFrequency (%)
B12035
15.1%
A10085
12.7%
48304
10.4%
38233
10.3%
C8111
10.2%
58085
10.2%
27920
10.0%
17244
9.1%
D5325
6.7%
E2858
 
3.6%
Other values (2)1372
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter39786
50.0%
Decimal Number39786
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B12035
30.2%
A10085
25.3%
C8111
20.4%
D5325
13.4%
E2858
 
7.2%
F1054
 
2.6%
G318
 
0.8%
Decimal Number
ValueCountFrequency (%)
48304
20.9%
38233
20.7%
58085
20.3%
27920
19.9%
17244
18.2%

Most occurring scripts

ValueCountFrequency (%)
Latin39786
50.0%
Common39786
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B12035
30.2%
A10085
25.3%
C8111
20.4%
D5325
13.4%
E2858
 
7.2%
F1054
 
2.6%
G318
 
0.8%
Common
ValueCountFrequency (%)
48304
20.9%
38233
20.7%
58085
20.3%
27920
19.9%
17244
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII79572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B12035
15.1%
A10085
12.7%
48304
10.4%
38233
10.3%
C8111
10.2%
58085
10.2%
27920
10.0%
17244
9.1%
D5325
6.7%
E2858
 
3.6%
Other values (2)1372
 
1.7%

emp_length
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
10+ years
9977 
< 1 year
4590 
2 years
4394 
3 years
4098 
4 years
3444 
Other values (6)
13283 

Length

Max length9
Median length7
Mean length7.535288795
Min length6

Characters and Unicode

Total characters299799
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row< 1 year
3rd row10+ years
4th row10+ years
5th row1 year

Common Values

ValueCountFrequency (%)
10+ years9977
25.1%
< 1 year4590
11.5%
2 years4394
11.0%
3 years4098
10.3%
4 years3444
 
8.7%
5 years3286
 
8.3%
1 year3247
 
8.2%
6 years2231
 
5.6%
7 years1775
 
4.5%
8 years1485
 
3.7%

Length

2021-07-27T08:43:07.075218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years31949
38.0%
109977
 
11.9%
17837
 
9.3%
year7837
 
9.3%
4590
 
5.5%
24394
 
5.2%
34098
 
4.9%
43444
 
4.1%
53286
 
3.9%
62231
 
2.7%
Other values (3)4519
 
5.4%

Most occurring characters

ValueCountFrequency (%)
44376
14.8%
y39786
13.3%
e39786
13.3%
a39786
13.3%
r39786
13.3%
s31949
10.7%
117814
5.9%
09977
 
3.3%
+9977
 
3.3%
<4590
 
1.5%
Other values (8)21972
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter191093
63.7%
Decimal Number49763
 
16.6%
Space Separator44376
 
14.8%
Math Symbol14567
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
117814
35.8%
09977
20.0%
24394
 
8.8%
34098
 
8.2%
43444
 
6.9%
53286
 
6.6%
62231
 
4.5%
71775
 
3.6%
81485
 
3.0%
91259
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
y39786
20.8%
e39786
20.8%
a39786
20.8%
r39786
20.8%
s31949
16.7%
Math Symbol
ValueCountFrequency (%)
+9977
68.5%
<4590
31.5%
Space Separator
ValueCountFrequency (%)
44376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin191093
63.7%
Common108706
36.3%

Most frequent character per script

Common
ValueCountFrequency (%)
44376
40.8%
117814
16.4%
09977
 
9.2%
+9977
 
9.2%
<4590
 
4.2%
24394
 
4.0%
34098
 
3.8%
43444
 
3.2%
53286
 
3.0%
62231
 
2.1%
Other values (3)4519
 
4.2%
Latin
ValueCountFrequency (%)
y39786
20.8%
e39786
20.8%
a39786
20.8%
r39786
20.8%
s31949
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII299799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44376
14.8%
y39786
13.3%
e39786
13.3%
a39786
13.3%
r39786
13.3%
s31949
10.7%
117814
5.9%
09977
 
3.3%
+9977
 
3.3%
<4590
 
1.5%
Other values (8)21972
7.3%

home_ownership
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
RENT
18918 
MORTGAGE
17703 
OWN
3064 
OTHER
 
98
NONE
 
3

Length

Max length8
Median length4
Mean length5.705273212
Min length3

Characters and Unicode

Total characters226990
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT18918
47.5%
MORTGAGE17703
44.5%
OWN3064
 
7.7%
OTHER98
 
0.2%
NONE3
 
< 0.1%

Length

2021-07-27T08:43:07.280951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:07.347198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
rent18918
47.5%
mortgage17703
44.5%
own3064
 
7.7%
other98
 
0.2%
none3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E36722
16.2%
R36719
16.2%
T36719
16.2%
G35406
15.6%
N21988
9.7%
O20868
9.2%
M17703
7.8%
A17703
7.8%
W3064
 
1.3%
H98
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter226990
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E36722
16.2%
R36719
16.2%
T36719
16.2%
G35406
15.6%
N21988
9.7%
O20868
9.2%
M17703
7.8%
A17703
7.8%
W3064
 
1.3%
H98
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin226990
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E36722
16.2%
R36719
16.2%
T36719
16.2%
G35406
15.6%
N21988
9.7%
O20868
9.2%
M17703
7.8%
A17703
7.8%
W3064
 
1.3%
H98
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII226990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E36722
16.2%
R36719
16.2%
T36719
16.2%
G35406
15.6%
N21988
9.7%
O20868
9.2%
M17703
7.8%
A17703
7.8%
W3064
 
1.3%
H98
 
< 0.1%

annual_inc
Real number (ℝ≥0)

SKEWED

Distinct5323
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68979.06676
Minimum4000
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:07.448856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q140500
median59000
Q382342.5
95-th percentile142000
Maximum6000000
Range5996000
Interquartile range (IQR)41842.5

Descriptive statistics

Standard deviation63762.63452
Coefficient of variation (CV)0.9243765902
Kurtosis2303.219566
Mean68979.06676
Median Absolute Deviation (MAD)20000
Skewness30.94133112
Sum2744401150
Variance4065673561
MonotonicityNot monotonic
2021-07-27T08:43:07.576206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600001507
 
3.8%
500001060
 
2.7%
40000876
 
2.2%
45000834
 
2.1%
30000825
 
2.1%
75000814
 
2.0%
65000804
 
2.0%
70000737
 
1.9%
48000724
 
1.8%
80000663
 
1.7%
Other values (5313)30942
77.8%
ValueCountFrequency (%)
40001
 
< 0.1%
40801
 
< 0.1%
42002
 
< 0.1%
48004
< 0.1%
48881
 
< 0.1%
50001
 
< 0.1%
55001
 
< 0.1%
60005
< 0.1%
70001
 
< 0.1%
72004
< 0.1%
ValueCountFrequency (%)
60000001
 
< 0.1%
39000001
 
< 0.1%
20397841
 
< 0.1%
19000001
 
< 0.1%
17820001
 
< 0.1%
14400001
 
< 0.1%
13620001
 
< 0.1%
12500001
 
< 0.1%
12000004
< 0.1%
11760001
 
< 0.1%

verification_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
Not Verified
16926 
Verified
12844 
Source Verified
10016 

Length

Max length15
Median length12
Mean length11.46393204
Min length8

Characters and Unicode

Total characters456104
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Not Verified16926
42.5%
Verified12844
32.3%
Source Verified10016
25.2%

Length

2021-07-27T08:43:07.805419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:07.879715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
verified39786
59.6%
not16926
25.4%
source10016
 
15.0%

Most occurring characters

ValueCountFrequency (%)
e89588
19.6%
i79572
17.4%
r49802
10.9%
V39786
8.7%
f39786
8.7%
d39786
8.7%
o26942
 
5.9%
26942
 
5.9%
N16926
 
3.7%
t16926
 
3.7%
Other values (3)30048
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter362434
79.5%
Uppercase Letter66728
 
14.6%
Space Separator26942
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e89588
24.7%
i79572
22.0%
r49802
13.7%
f39786
11.0%
d39786
11.0%
o26942
 
7.4%
t16926
 
4.7%
u10016
 
2.8%
c10016
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
V39786
59.6%
N16926
25.4%
S10016
 
15.0%
Space Separator
ValueCountFrequency (%)
26942
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin429162
94.1%
Common26942
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e89588
20.9%
i79572
18.5%
r49802
11.6%
V39786
9.3%
f39786
9.3%
d39786
9.3%
o26942
 
6.3%
N16926
 
3.9%
t16926
 
3.9%
S10016
 
2.3%
Other values (2)20032
 
4.7%
Common
ValueCountFrequency (%)
26942
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII456104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e89588
19.6%
i79572
17.4%
r49802
10.9%
V39786
8.7%
f39786
8.7%
d39786
8.7%
o26942
 
5.9%
26942
 
5.9%
N16926
 
3.7%
t16926
 
3.7%
Other values (3)30048
 
6.6%

issue_d
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
Dec-11
 
2267
Nov-11
 
2232
Oct-11
 
2118
Sep-11
 
2067
Aug-11
 
1934
Other values (50)
29168 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238716
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDec-11
2nd rowDec-11
3rd rowDec-11
4th rowDec-11
5th rowDec-11

Common Values

ValueCountFrequency (%)
Dec-112267
 
5.7%
Nov-112232
 
5.6%
Oct-112118
 
5.3%
Sep-112067
 
5.2%
Aug-111934
 
4.9%
Jul-111875
 
4.7%
Jun-111835
 
4.6%
May-111704
 
4.3%
Apr-111563
 
3.9%
Mar-111448
 
3.6%
Other values (45)20743
52.1%

Length

2021-07-27T08:43:08.095097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec-112267
 
5.7%
nov-112232
 
5.6%
oct-112118
 
5.3%
sep-112067
 
5.2%
aug-111934
 
4.9%
jul-111875
 
4.7%
jun-111835
 
4.6%
may-111704
 
4.3%
apr-111563
 
3.9%
mar-111448
 
3.6%
Other values (45)20743
52.1%

Most occurring characters

ValueCountFrequency (%)
154978
23.0%
-39786
16.7%
018065
 
7.6%
e10454
 
4.4%
u10292
 
4.3%
J9147
 
3.8%
c8381
 
3.5%
a8090
 
3.4%
p6488
 
2.7%
A6359
 
2.7%
Other values (18)66676
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79572
33.3%
Decimal Number79572
33.3%
Uppercase Letter39786
16.7%
Dash Punctuation39786
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10454
13.1%
u10292
12.9%
c8381
10.5%
a8090
10.2%
p6488
8.2%
n5666
7.1%
r5532
7.0%
o4176
 
5.2%
v4176
 
5.2%
t3939
 
5.0%
Other values (4)12378
15.6%
Uppercase Letter
ValueCountFrequency (%)
J9147
23.0%
A6359
16.0%
M5711
14.4%
D4442
11.2%
N4176
10.5%
O3939
9.9%
S3653
 
9.2%
F2359
 
5.9%
Decimal Number
ValueCountFrequency (%)
154978
69.1%
018065
 
22.7%
94716
 
5.9%
81562
 
2.0%
7251
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
-39786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119358
50.0%
Common119358
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10454
 
8.8%
u10292
 
8.6%
J9147
 
7.7%
c8381
 
7.0%
a8090
 
6.8%
p6488
 
5.4%
A6359
 
5.3%
M5711
 
4.8%
n5666
 
4.7%
r5532
 
4.6%
Other values (12)43238
36.2%
Common
ValueCountFrequency (%)
154978
46.1%
-39786
33.3%
018065
 
15.1%
94716
 
4.0%
81562
 
1.3%
7251
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII238716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
154978
23.0%
-39786
16.7%
018065
 
7.6%
e10454
 
4.4%
u10292
 
4.3%
J9147
 
3.8%
c8381
 
3.5%
a8090
 
3.4%
p6488
 
2.7%
A6359
 
2.7%
Other values (18)66676
27.9%

loan_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
Fully Paid
34108 
Charged Off
5662 
Late (31-120 days)
 
10
Current
 
3
Default
 
1
Other values (2)
 
2

Length

Max length18
Median length10
Mean length10.14432212
Min length7

Characters and Unicode

Total characters403602
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowFully Paid
2nd rowCharged Off
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid34108
85.7%
Charged Off5662
 
14.2%
Late (31-120 days)10
 
< 0.1%
Current3
 
< 0.1%
Default1
 
< 0.1%
Late (16-30 days)1
 
< 0.1%
In Grace Period1
 
< 0.1%

Length

2021-07-27T08:43:08.298485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:08.374444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
paid34108
42.9%
fully34108
42.9%
charged5662
 
7.1%
off5662
 
7.1%
days11
 
< 0.1%
late11
 
< 0.1%
31-12010
 
< 0.1%
current3
 
< 0.1%
grace1
 
< 0.1%
in1
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l68217
16.9%
39794
9.9%
a39794
9.9%
d39782
9.9%
y34119
8.5%
u34112
8.5%
P34109
8.5%
i34109
8.5%
F34108
8.5%
f11325
 
2.8%
Other values (23)34133
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter284163
70.4%
Uppercase Letter79558
 
19.7%
Space Separator39794
 
9.9%
Decimal Number54
 
< 0.1%
Open Punctuation11
 
< 0.1%
Dash Punctuation11
 
< 0.1%
Close Punctuation11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l68217
24.0%
a39794
14.0%
d39782
14.0%
y34119
12.0%
u34112
12.0%
i34109
12.0%
f11325
 
4.0%
e5679
 
2.0%
r5670
 
2.0%
h5662
 
2.0%
Other values (6)5694
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
P34109
42.9%
F34108
42.9%
C5665
 
7.1%
O5662
 
7.1%
L11
 
< 0.1%
I1
 
< 0.1%
G1
 
< 0.1%
D1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
121
38.9%
311
20.4%
011
20.4%
210
18.5%
61
 
1.9%
Space Separator
ValueCountFrequency (%)
39794
100.0%
Open Punctuation
ValueCountFrequency (%)
(11
100.0%
Dash Punctuation
ValueCountFrequency (%)
-11
100.0%
Close Punctuation
ValueCountFrequency (%)
)11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin363721
90.1%
Common39881
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
l68217
18.8%
a39794
10.9%
d39782
10.9%
y34119
9.4%
u34112
9.4%
P34109
9.4%
i34109
9.4%
F34108
9.4%
f11325
 
3.1%
e5679
 
1.6%
Other values (14)28367
7.8%
Common
ValueCountFrequency (%)
39794
99.8%
121
 
0.1%
(11
 
< 0.1%
311
 
< 0.1%
-11
 
< 0.1%
011
 
< 0.1%
)11
 
< 0.1%
210
 
< 0.1%
61
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII403602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l68217
16.9%
39794
9.9%
a39794
9.9%
d39782
9.9%
y34119
8.5%
u34112
8.5%
P34109
8.5%
i34109
8.5%
F34108
8.5%
f11325
 
2.8%
Other values (23)34133
8.5%

pymnt_plan
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
False
39786 
ValueCountFrequency (%)
False39786
100.0%
2021-07-27T08:43:08.449434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

zip_code
Categorical

HIGH CARDINALITY

Distinct823
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
100xx
 
597
945xx
 
546
112xx
 
517
606xx
 
503
070xx
 
473
Other values (818)
37150 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters198930
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.1%

Sample

1st row860xx
2nd row309xx
3rd row606xx
4th row917xx
5th row972xx

Common Values

ValueCountFrequency (%)
100xx597
 
1.5%
945xx546
 
1.4%
112xx517
 
1.3%
606xx503
 
1.3%
070xx473
 
1.2%
900xx453
 
1.1%
021xx397
 
1.0%
300xx395
 
1.0%
926xx371
 
0.9%
750xx368
 
0.9%
Other values (813)35166
88.4%

Length

2021-07-27T08:43:08.654090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100xx597
 
1.5%
945xx546
 
1.4%
112xx517
 
1.3%
606xx503
 
1.3%
070xx473
 
1.2%
900xx453
 
1.1%
021xx397
 
1.0%
300xx395
 
1.0%
926xx371
 
0.9%
750xx368
 
0.9%
Other values (813)35166
88.4%

Most occurring characters

ValueCountFrequency (%)
x79572
40.0%
019803
 
10.0%
115655
 
7.9%
213610
 
6.8%
912695
 
6.4%
312381
 
6.2%
710280
 
5.2%
49137
 
4.6%
59036
 
4.5%
88691
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number119358
60.0%
Lowercase Letter79572
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019803
16.6%
115655
13.1%
213610
11.4%
912695
10.6%
312381
10.4%
710280
8.6%
49137
7.7%
59036
7.6%
88691
7.3%
68070
6.8%
Lowercase Letter
ValueCountFrequency (%)
x79572
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common119358
60.0%
Latin79572
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019803
16.6%
115655
13.1%
213610
11.4%
912695
10.6%
312381
10.4%
710280
8.6%
49137
7.7%
59036
7.6%
88691
7.3%
68070
6.8%
Latin
ValueCountFrequency (%)
x79572
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII198930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x79572
40.0%
019803
 
10.0%
115655
 
7.9%
213610
 
6.8%
912695
 
6.4%
312381
 
6.2%
710280
 
5.2%
49137
 
4.6%
59036
 
4.5%
88691
 
4.4%

addr_state
Categorical

HIGH CORRELATION

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
CA
7105 
NY
3817 
FL
2872 
TX
2734 
NJ
 
1855
Other values (45)
21403 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79572
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowGA
3rd rowIL
4th rowCA
5th rowOR

Common Values

ValueCountFrequency (%)
CA7105
17.9%
NY3817
 
9.6%
FL2872
 
7.2%
TX2734
 
6.9%
NJ1855
 
4.7%
IL1525
 
3.8%
PA1519
 
3.8%
VA1408
 
3.5%
GA1399
 
3.5%
MA1344
 
3.4%
Other values (40)14208
35.7%

Length

2021-07-27T08:43:08.906272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca7105
17.9%
ny3817
 
9.6%
fl2872
 
7.2%
tx2734
 
6.9%
nj1855
 
4.7%
il1525
 
3.8%
pa1519
 
3.8%
va1408
 
3.5%
ga1399
 
3.5%
ma1344
 
3.4%
Other values (40)14208
35.7%

Most occurring characters

ValueCountFrequency (%)
A15719
19.8%
C10126
12.7%
N7968
10.0%
L5285
 
6.6%
M4720
 
5.9%
Y4227
 
5.3%
T3903
 
4.9%
O3455
 
4.3%
I3100
 
3.9%
F2872
 
3.6%
Other values (14)18197
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter79572
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A15719
19.8%
C10126
12.7%
N7968
10.0%
L5285
 
6.6%
M4720
 
5.9%
Y4227
 
5.3%
T3903
 
4.9%
O3455
 
4.3%
I3100
 
3.9%
F2872
 
3.6%
Other values (14)18197
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin79572
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A15719
19.8%
C10126
12.7%
N7968
10.0%
L5285
 
6.6%
M4720
 
5.9%
Y4227
 
5.3%
T3903
 
4.9%
O3455
 
4.3%
I3100
 
3.9%
F2872
 
3.6%
Other values (14)18197
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII79572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A15719
19.8%
C10126
12.7%
N7968
10.0%
L5285
 
6.6%
M4720
 
5.9%
Y4227
 
5.3%
T3903
 
4.9%
O3455
 
4.3%
I3100
 
3.9%
F2872
 
3.6%
Other values (14)18197
22.9%

dti
Real number (ℝ≥0)

Distinct2868
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.31779395
Minimum0
Maximum29.99
Zeros184
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:09.022031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.13
Q18.18
median13.41
Q318.6
95-th percentile23.84
Maximum29.99
Range29.99
Interquartile range (IQR)10.42

Descriptive statistics

Standard deviation6.678300272
Coefficient of variation (CV)0.5014569454
Kurtosis-0.8508298161
Mean13.31779395
Median Absolute Deviation (MAD)5.21
Skewness-0.02776106326
Sum529861.75
Variance44.59969452
MonotonicityNot monotonic
2021-07-27T08:43:09.146928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0184
 
0.5%
1251
 
0.1%
1846
 
0.1%
19.240
 
0.1%
13.239
 
0.1%
16.838
 
0.1%
13.538
 
0.1%
12.4838
 
0.1%
637
 
0.1%
14.2936
 
0.1%
Other values (2858)39239
98.6%
ValueCountFrequency (%)
0184
0.5%
0.013
 
< 0.1%
0.025
 
< 0.1%
0.032
 
< 0.1%
0.043
 
< 0.1%
0.052
 
< 0.1%
0.061
 
< 0.1%
0.075
 
< 0.1%
0.085
 
< 0.1%
0.093
 
< 0.1%
ValueCountFrequency (%)
29.991
 
< 0.1%
29.951
 
< 0.1%
29.933
< 0.1%
29.922
< 0.1%
29.891
 
< 0.1%
29.881
 
< 0.1%
29.862
< 0.1%
29.851
 
< 0.1%
29.831
 
< 0.1%
29.821
 
< 0.1%

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1465339567
Minimum0
Maximum11
Zeros35466
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:09.258966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4918260362
Coefficient of variation (CV)3.356396342
Kurtosis39.36795255
Mean0.1465339567
Median Absolute Deviation (MAD)0
Skewness5.019746389
Sum5830
Variance0.2418928499
MonotonicityNot monotonic
2021-07-27T08:43:09.348180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
035466
89.1%
13309
 
8.3%
2688
 
1.7%
3221
 
0.6%
462
 
0.2%
522
 
0.1%
610
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
035466
89.1%
13309
 
8.3%
2688
 
1.7%
3221
 
0.6%
462
 
0.2%
522
 
0.1%
610
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
74
 
< 0.1%
610
 
< 0.1%
522
 
0.1%
462
 
0.2%
3221
 
0.6%
2688
 
1.7%
13309
8.3%

earliest_cr_line
Categorical

HIGH CARDINALITY

Distinct526
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
Nov-98
 
371
Oct-99
 
366
Dec-98
 
349
Oct-00
 
346
Dec-97
 
329
Other values (521)
38025 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238716
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.1%

Sample

1st rowJan-85
2nd rowApr-99
3rd rowNov-01
4th rowFeb-96
5th rowJan-96

Common Values

ValueCountFrequency (%)
Nov-98371
 
0.9%
Oct-99366
 
0.9%
Dec-98349
 
0.9%
Oct-00346
 
0.9%
Dec-97329
 
0.8%
Nov-99320
 
0.8%
Nov-00320
 
0.8%
Oct-98307
 
0.8%
Sep-00306
 
0.8%
Nov-97299
 
0.8%
Other values (516)36473
91.7%

Length

2021-07-27T08:43:09.601196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nov-98371
 
0.9%
oct-99366
 
0.9%
dec-98349
 
0.9%
oct-00346
 
0.9%
dec-97329
 
0.8%
nov-00320
 
0.8%
nov-99320
 
0.8%
oct-98307
 
0.8%
sep-00306
 
0.8%
nov-97299
 
0.8%
Other values (516)36473
91.7%

Most occurring characters

ValueCountFrequency (%)
-39786
16.7%
923398
 
9.8%
019383
 
8.1%
e10564
 
4.4%
J9441
 
4.0%
u9319
 
3.9%
a9137
 
3.8%
88477
 
3.6%
c8160
 
3.4%
n6374
 
2.7%
Other values (23)94677
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79572
33.3%
Decimal Number79572
33.3%
Uppercase Letter39786
16.7%
Dash Punctuation39786
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10564
13.3%
u9319
11.7%
a9137
11.5%
c8160
10.3%
n6374
8.0%
p6346
8.0%
r5543
7.0%
t4082
 
5.1%
o3937
 
4.9%
v3937
 
4.9%
Other values (4)12173
15.3%
Decimal Number
ValueCountFrequency (%)
923398
29.4%
019383
24.4%
88477
 
10.7%
74829
 
6.1%
44286
 
5.4%
54211
 
5.3%
64178
 
5.3%
33793
 
4.8%
13740
 
4.7%
23277
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
J9441
23.7%
A6060
15.2%
M5702
14.3%
O4082
10.3%
D4078
10.2%
N3937
9.9%
S3599
 
9.0%
F2887
 
7.3%
Dash Punctuation
ValueCountFrequency (%)
-39786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119358
50.0%
Common119358
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10564
 
8.9%
J9441
 
7.9%
u9319
 
7.8%
a9137
 
7.7%
c8160
 
6.8%
n6374
 
5.3%
p6346
 
5.3%
A6060
 
5.1%
M5702
 
4.8%
r5543
 
4.6%
Other values (12)42712
35.8%
Common
ValueCountFrequency (%)
-39786
33.3%
923398
19.6%
019383
16.2%
88477
 
7.1%
74829
 
4.0%
44286
 
3.6%
54211
 
3.5%
64178
 
3.5%
33793
 
3.2%
13740
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII238716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-39786
16.7%
923398
 
9.8%
019383
 
8.1%
e10564
 
4.4%
J9441
 
4.0%
u9319
 
3.9%
a9137
 
3.8%
88477
 
3.6%
c8160
 
3.4%
n6374
 
2.7%
Other values (23)94677
39.7%

inq_last_6mths
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8690494144
Minimum0
Maximum8
Zeros19337
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:09.691969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.070069307
Coefficient of variation (CV)1.231310083
Kurtosis2.559164109
Mean0.8690494144
Median Absolute Deviation (MAD)1
Skewness1.389834879
Sum34576
Variance1.145048321
MonotonicityNot monotonic
2021-07-27T08:43:09.784872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
019337
48.6%
110986
27.6%
25824
 
14.6%
33053
 
7.7%
4326
 
0.8%
5146
 
0.4%
664
 
0.2%
735
 
0.1%
815
 
< 0.1%
ValueCountFrequency (%)
019337
48.6%
110986
27.6%
25824
 
14.6%
33053
 
7.7%
4326
 
0.8%
5146
 
0.4%
664
 
0.2%
735
 
0.1%
815
 
< 0.1%
ValueCountFrequency (%)
815
 
< 0.1%
735
 
0.1%
664
 
0.2%
5146
 
0.4%
4326
 
0.8%
33053
 
7.7%
25824
 
14.6%
110986
27.6%
019337
48.6%

open_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.294023023
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:09.901761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q312
95-th percentile17
Maximum44
Range42
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.399996911
Coefficient of variation (CV)0.4734222091
Kurtosis1.676570497
Mean9.294023023
Median Absolute Deviation (MAD)3
Skewness1.003459847
Sum369772
Variance19.35997282
MonotonicityNot monotonic
2021-07-27T08:43:10.017454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
74025
10.1%
63954
9.9%
83944
9.9%
93727
9.4%
103227
 
8.1%
53185
 
8.0%
112750
 
6.9%
42346
 
5.9%
122279
 
5.7%
131915
 
4.8%
Other values (30)8434
21.2%
ValueCountFrequency (%)
2608
 
1.5%
31496
 
3.8%
42346
5.9%
53185
8.0%
63954
9.9%
74025
10.1%
83944
9.9%
93727
9.4%
103227
8.1%
112750
6.9%
ValueCountFrequency (%)
441
 
< 0.1%
421
 
< 0.1%
411
 
< 0.1%
391
 
< 0.1%
381
 
< 0.1%
362
 
< 0.1%
354
< 0.1%
345
< 0.1%
333
< 0.1%
324
< 0.1%

pub_rec
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
0
37665 
1
 
2060
2
 
51
3
 
8
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39786
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
037665
94.7%
12060
 
5.2%
251
 
0.1%
38
 
< 0.1%
42
 
< 0.1%

Length

2021-07-27T08:43:10.253569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:10.325278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
037665
94.7%
12060
 
5.2%
251
 
0.1%
38
 
< 0.1%
42
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
037665
94.7%
12060
 
5.2%
251
 
0.1%
38
 
< 0.1%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39786
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037665
94.7%
12060
 
5.2%
251
 
0.1%
38
 
< 0.1%
42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common39786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
037665
94.7%
12060
 
5.2%
251
 
0.1%
38
 
< 0.1%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII39786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037665
94.7%
12060
 
5.2%
251
 
0.1%
38
 
< 0.1%
42
 
< 0.1%

revol_bal
Real number (ℝ≥0)

ZEROS

Distinct21738
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13391.98391
Minimum0
Maximum149588
Zeros996
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:10.427067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile321.25
Q13704.25
median8859.5
Q317065
95-th percentile41677.5
Maximum149588
Range149588
Interquartile range (IQR)13360.75

Descriptive statistics

Standard deviation15894.63511
Coefficient of variation (CV)1.186876807
Kurtosis14.87369487
Mean13391.98391
Median Absolute Deviation (MAD)6035.5
Skewness3.189403139
Sum532813472
Variance252639425.2
MonotonicityNot monotonic
2021-07-27T08:43:10.559385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0996
 
2.5%
25514
 
< 0.1%
29814
 
< 0.1%
112
 
< 0.1%
68211
 
< 0.1%
529
 
< 0.1%
7989
 
< 0.1%
109
 
< 0.1%
3469
 
< 0.1%
69
 
< 0.1%
Other values (21728)38694
97.3%
ValueCountFrequency (%)
0996
2.5%
112
 
< 0.1%
25
 
< 0.1%
36
 
< 0.1%
43
 
< 0.1%
58
 
< 0.1%
69
 
< 0.1%
75
 
< 0.1%
85
 
< 0.1%
98
 
< 0.1%
ValueCountFrequency (%)
1495881
< 0.1%
1495271
< 0.1%
1490001
< 0.1%
1488291
< 0.1%
1488041
< 0.1%
1478971
< 0.1%
1477501
< 0.1%
1475591
< 0.1%
1474511
< 0.1%
1473651
< 0.1%

revol_util
Real number (ℝ≥0)

ZEROS

Distinct1090
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.85812336
Minimum0
Maximum99.9
Zeros980
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:10.695671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7
Q125.5
median49.3
Q372.4
95-th percentile93.5
Maximum99.9
Range99.9
Interquartile range (IQR)46.9

Descriptive statistics

Standard deviation28.31881886
Coefficient of variation (CV)0.5796133153
Kurtosis-1.1033789
Mean48.85812336
Median Absolute Deviation (MAD)23.4
Skewness-0.03495265605
Sum1943869.296
Variance801.9555018
MonotonicityNot monotonic
2021-07-27T08:43:10.824303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0980
 
2.5%
0.263
 
0.2%
6362
 
0.2%
40.759
 
0.1%
0.158
 
0.1%
66.758
 
0.1%
31.257
 
0.1%
66.657
 
0.1%
70.457
 
0.1%
46.457
 
0.1%
Other values (1080)38278
96.2%
ValueCountFrequency (%)
0980
2.5%
0.011
 
< 0.1%
0.031
 
< 0.1%
0.041
 
< 0.1%
0.051
 
< 0.1%
0.158
 
0.1%
0.121
 
< 0.1%
0.161
 
< 0.1%
0.263
 
0.2%
0.342
 
0.1%
ValueCountFrequency (%)
99.926
0.1%
99.823
0.1%
99.731
0.1%
99.624
0.1%
99.524
0.1%
99.421
0.1%
99.330
0.1%
99.217
< 0.1%
99.130
0.1%
9932
0.1%

total_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct82
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.09030815
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:10.953975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median20
Q329
95-th percentile43
Maximum90
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.40162035
Coefficient of variation (CV)0.5161367725
Kurtosis0.6931800448
Mean22.09030815
Median Absolute Deviation (MAD)7
Skewness0.8273519184
Sum878885
Variance129.9969467
MonotonicityNot monotonic
2021-07-27T08:43:11.079127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161475
 
3.7%
151465
 
3.7%
171461
 
3.7%
141449
 
3.6%
201430
 
3.6%
181423
 
3.6%
211416
 
3.6%
131388
 
3.5%
191343
 
3.4%
121327
 
3.3%
Other values (72)25609
64.4%
ValueCountFrequency (%)
24
 
< 0.1%
3182
 
0.5%
4420
 
1.1%
5553
1.4%
6684
1.7%
7831
2.1%
81006
2.5%
91081
2.7%
101194
3.0%
111279
3.2%
ValueCountFrequency (%)
901
< 0.1%
871
< 0.1%
811
< 0.1%
801
< 0.1%
792
< 0.1%
781
< 0.1%
771
< 0.1%
762
< 0.1%
752
< 0.1%
741
< 0.1%

initial_list_status
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
False
39786 
ValueCountFrequency (%)
False39786
100.0%
2021-07-27T08:43:11.160912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

out_prncp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2436520384
Minimum0
Maximum1974.73
Zeros39770
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:11.222462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1974.73
Range1974.73
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.51972993
Coefficient of variation (CV)67.80049961
Kurtosis8602.865471
Mean0.2436520384
Median Absolute Deviation (MAD)0
Skewness87.30770026
Sum9693.94
Variance272.9014771
MonotonicityNot monotonic
2021-07-27T08:43:11.328717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
039770
> 99.9%
5.991
 
< 0.1%
156.661
 
< 0.1%
694.411
 
< 0.1%
77.231
 
< 0.1%
945.461
 
< 0.1%
496.251
 
< 0.1%
415.311
 
< 0.1%
1429.291
 
< 0.1%
1480.791
 
< 0.1%
Other values (7)7
 
< 0.1%
ValueCountFrequency (%)
039770
> 99.9%
5.991
 
< 0.1%
26.061
 
< 0.1%
77.231
 
< 0.1%
115.131
 
< 0.1%
156.661
 
< 0.1%
409.051
 
< 0.1%
415.311
 
< 0.1%
488.11
 
< 0.1%
488.61
 
< 0.1%
ValueCountFrequency (%)
1974.731
< 0.1%
1480.791
< 0.1%
1429.291
< 0.1%
945.461
< 0.1%
694.411
< 0.1%
496.251
< 0.1%
490.881
< 0.1%
488.61
< 0.1%
488.11
< 0.1%
415.311
< 0.1%

out_prncp_inv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.243007842
Minimum0
Maximum1972.73
Zeros39770
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:11.443470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1972.73
Range1972.73
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.48698512
Coefficient of variation (CV)67.84548592
Kurtosis8627.628325
Mean0.243007842
Median Absolute Deviation (MAD)0
Skewness87.43436316
Sum9668.31
Variance271.8206783
MonotonicityNot monotonic
2021-07-27T08:43:11.548635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
039770
> 99.9%
415.311
 
< 0.1%
77.231
 
< 0.1%
484.991
 
< 0.1%
1477.091
 
< 0.1%
1972.731
 
< 0.1%
945.461
 
< 0.1%
496.251
 
< 0.1%
5.991
 
< 0.1%
26.061
 
< 0.1%
Other values (7)7
 
< 0.1%
ValueCountFrequency (%)
039770
> 99.9%
5.991
 
< 0.1%
26.061
 
< 0.1%
77.231
 
< 0.1%
114.921
 
< 0.1%
156.661
 
< 0.1%
408.381
 
< 0.1%
415.311
 
< 0.1%
484.991
 
< 0.1%
487.891
 
< 0.1%
ValueCountFrequency (%)
1972.731
< 0.1%
1477.091
< 0.1%
1429.291
< 0.1%
945.461
< 0.1%
681.961
< 0.1%
496.251
< 0.1%
488.11
< 0.1%
487.891
< 0.1%
484.991
< 0.1%
415.311
< 0.1%

total_pymnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct37820
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12229.74916
Minimum0
Maximum58886.47343
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:11.676596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1891.43
Q15583.267107
median9934.743269
Q316626.21851
95-th percentile30502.43109
Maximum58886.47343
Range58886.47343
Interquartile range (IQR)11042.9514

Descriptive statistics

Standard deviation9165.377882
Coefficient of variation (CV)0.7494330231
Kurtosis2.137515412
Mean12229.74916
Median Absolute Deviation (MAD)5052.34814
Skewness1.371597465
Sum486572800
Variance84004151.73
MonotonicityNot monotonic
2021-07-27T08:43:11.802345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11196.5694326
 
0.1%
016
 
< 0.1%
10956.7759616
 
< 0.1%
11784.2322316
 
< 0.1%
5478.38798115
 
< 0.1%
13148.1378615
 
< 0.1%
5557.02554313
 
< 0.1%
13435.9002113
 
< 0.1%
13263.9546412
 
< 0.1%
5598.28471311
 
< 0.1%
Other values (37810)39633
99.6%
ValueCountFrequency (%)
016
< 0.1%
33.971
 
< 0.1%
35.91
 
< 0.1%
44.922
 
< 0.1%
44.961
 
< 0.1%
61.711
 
< 0.1%
62.861
 
< 0.1%
66.941
 
< 0.1%
67.321
 
< 0.1%
69.781
 
< 0.1%
ValueCountFrequency (%)
58886.473431
< 0.1%
58563.679931
< 0.1%
58480.139921
< 0.1%
58133.31991
< 0.1%
58090.952071
< 0.1%
58071.199821
< 0.1%
58071.199771
< 0.1%
57997.279951
< 0.1%
57835.279911
< 0.1%
57143.259961
< 0.1%

total_pymnt_inv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct37555
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11643.38972
Minimum0
Maximum58563.68
Zeros165
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:11.940556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1424.37
Q15121.1525
median9311.345
Q315904.7975
95-th percentile29896.3675
Maximum58563.68
Range58563.68
Interquartile range (IQR)10783.645

Descriptive statistics

Standard deviation9068.751337
Coefficient of variation (CV)0.7788755297
Kurtosis2.189926957
Mean11643.38972
Median Absolute Deviation (MAD)4962.965
Skewness1.388077605
Sum463243903.4
Variance82242250.81
MonotonicityNot monotonic
2021-07-27T08:43:12.072429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0165
 
0.4%
6514.5216
 
< 0.1%
5478.3914
 
< 0.1%
13148.1414
 
< 0.1%
11196.5712
 
< 0.1%
10956.7812
 
< 0.1%
6717.9512
 
< 0.1%
5557.0311
 
< 0.1%
7328.9211
 
< 0.1%
13517.3611
 
< 0.1%
Other values (37545)39508
99.3%
ValueCountFrequency (%)
0165
0.4%
0.541
 
< 0.1%
12.651
 
< 0.1%
18.971
 
< 0.1%
21.61
 
< 0.1%
25.181
 
< 0.1%
26.191
 
< 0.1%
33.971
 
< 0.1%
33.991
 
< 0.1%
35.91
 
< 0.1%
ValueCountFrequency (%)
58563.681
< 0.1%
58514.931
< 0.1%
58438.371
< 0.1%
58056.41
< 0.1%
57967.531
< 0.1%
57953.691
< 0.1%
57863.511
< 0.1%
57672.741
< 0.1%
57628.731
< 0.1%
57143.261
< 0.1%

total_rec_prncp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6920
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9855.330536
Minimum0
Maximum35000.02
Zeros74
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:12.210461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1342.9175
Q14620.91
median8000
Q314000
95-th percentile25000
Maximum35000.02
Range35000.02
Interquartile range (IQR)9379.09

Descriptive statistics

Standard deviation7143.226346
Coefficient of variation (CV)0.7248083989
Kurtosis1.120339938
Mean9855.330536
Median Absolute Deviation (MAD)4000
Skewness1.127789978
Sum392104180.7
Variance51025682.63
MonotonicityNot monotonic
2021-07-27T08:43:12.342666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002316
 
5.8%
120001889
 
4.7%
50001726
 
4.3%
60001659
 
4.2%
150001453
 
3.7%
80001338
 
3.4%
200001146
 
2.9%
4000964
 
2.4%
3000895
 
2.2%
7000861
 
2.2%
Other values (6910)25539
64.2%
ValueCountFrequency (%)
074
0.2%
21.211
 
< 0.1%
21.931
 
< 0.1%
22.241
 
< 0.1%
22.51
 
< 0.1%
24.871
 
< 0.1%
30.321
 
< 0.1%
32.511
 
< 0.1%
34.51
 
< 0.1%
35.141
 
< 0.1%
ValueCountFrequency (%)
35000.022
 
< 0.1%
35000.011
 
< 0.1%
35000417
1.0%
34999.997
 
< 0.1%
34999.981
 
< 0.1%
34999.971
 
< 0.1%
34997.121
 
< 0.1%
348001
 
< 0.1%
346751
 
< 0.1%
345251
 
< 0.1%

total_rec_int
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct35047
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2276.328632
Minimum0
Maximum23886.47
Zeros71
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:12.481155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186.57
Q1663.045
median1352.805
Q32845.5675
95-th percentile7615.6875
Maximum23886.47
Range23886.47
Interquartile range (IQR)2182.5225

Descriptive statistics

Standard deviation2632.387428
Coefficient of variation (CV)1.156418011
Kurtosis9.755509659
Mean2276.328632
Median Absolute Deviation (MAD)869.235
Skewness2.679189534
Sum90566010.94
Variance6929463.573
MonotonicityNot monotonic
2021-07-27T08:43:12.622524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071
 
0.2%
1196.5726
 
0.1%
514.5219
 
< 0.1%
717.9517
 
< 0.1%
1148.1417
 
< 0.1%
1784.2317
 
< 0.1%
956.7817
 
< 0.1%
478.3916
 
< 0.1%
1907.3514
 
< 0.1%
632.2113
 
< 0.1%
Other values (35037)39559
99.4%
ValueCountFrequency (%)
071
0.2%
6.221
 
< 0.1%
6.271
 
< 0.1%
7.191
 
< 0.1%
7.22
 
< 0.1%
8.231
 
< 0.1%
9.341
 
< 0.1%
9.491
 
< 0.1%
9.582
 
< 0.1%
10.261
 
< 0.1%
ValueCountFrequency (%)
23886.471
< 0.1%
23563.681
< 0.1%
23480.141
< 0.1%
23090.951
< 0.1%
23084.931
< 0.1%
23071.22
< 0.1%
22997.281
< 0.1%
22835.281
< 0.1%
22700.391
< 0.1%
22143.261
< 0.1%

total_rec_late_fee
Real number (ℝ≥0)

ZEROS

Distinct1364
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.388346185
Minimum0
Maximum180.2
Zeros37708
Zeros (%)94.8%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:12.755426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.9400886
Maximum180.2
Range180.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.397534781
Coefficient of variation (CV)5.328307063
Kurtosis102.7203214
Mean1.388346185
Median Absolute Deviation (MAD)0
Skewness8.475550177
Sum55236.7413
Variance54.72352084
MonotonicityNot monotonic
2021-07-27T08:43:12.890775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037708
94.8%
15258
 
0.6%
15.0000000163
 
0.2%
3054
 
0.1%
15.0000000248
 
0.1%
14.9999999940
 
0.1%
14.9999999835
 
0.1%
15.0000000333
 
0.1%
15.0000000426
 
0.1%
14.9999999725
 
0.1%
Other values (1354)1496
 
3.8%
ValueCountFrequency (%)
037708
94.8%
0.011
 
< 0.1%
0.0607997511
 
< 0.1%
0.0737871041
 
< 0.1%
0.1017045621
 
< 0.1%
0.1399999991
 
< 0.1%
0.1800829041
 
< 0.1%
0.184773621
 
< 0.1%
0.271
 
< 0.1%
0.3020365531
 
< 0.1%
ValueCountFrequency (%)
180.21
< 0.1%
170.76000041
< 0.1%
166.42971071
< 0.1%
165.691
< 0.1%
146.60000031
< 0.1%
146.041
< 0.1%
134.07000071
< 0.1%
130.59703721
< 0.1%
130.471
< 0.1%
127.78781361
< 0.1%

recoveries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4571
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.70172637
Minimum0
Maximum29623.35
Zeros34186
Zeros (%)85.9%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:13.036817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile378.2425
Maximum29623.35
Range29623.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation695.7286586
Coefficient of variation (CV)7.194583641
Kurtosis379.9767767
Mean96.70172637
Median Absolute Deviation (MAD)0
Skewness16.54855892
Sum3847374.885
Variance484038.3664
MonotonicityNot monotonic
2021-07-27T08:43:13.175473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034186
85.9%
0.313
 
< 0.1%
1.810
 
< 0.1%
4.29
 
< 0.1%
1.29
 
< 0.1%
0.728
 
< 0.1%
2.48
 
< 0.1%
1.768
 
< 0.1%
1.158
 
< 0.1%
3.367
 
< 0.1%
Other values (4561)5520
 
13.9%
ValueCountFrequency (%)
034186
85.9%
0.013
 
< 0.1%
0.023
 
< 0.1%
0.033
 
< 0.1%
0.045
 
< 0.1%
0.052
 
< 0.1%
0.065
 
< 0.1%
0.073
 
< 0.1%
0.082
 
< 0.1%
0.094
 
< 0.1%
ValueCountFrequency (%)
29623.351
< 0.1%
22943.371
< 0.1%
21811.731
< 0.1%
21110.861
< 0.1%
20008.41
< 0.1%
19916.781
< 0.1%
19508.261
< 0.1%
18694.791
< 0.1%
16503.121
< 0.1%
16268.351
< 0.1%

collection_recovery_fee
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2663
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.62538968
Minimum0
Maximum7002.19
Zeros35958
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:13.318153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.364800002
Maximum7002.19
Range7002.19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation149.8555039
Coefficient of variation (CV)11.86937653
Kurtosis804.9632081
Mean12.62538968
Median Absolute Deviation (MAD)0
Skewness24.82250475
Sum502313.7537
Variance22456.67204
MonotonicityNot monotonic
2021-07-27T08:43:13.452899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035958
90.4%
212
 
< 0.1%
1.210
 
< 0.1%
3.719
 
< 0.1%
2.028
 
< 0.1%
1.698
 
< 0.1%
1.888
 
< 0.1%
0.88
 
< 0.1%
1.68
 
< 0.1%
2.528
 
< 0.1%
Other values (2653)3749
 
9.4%
ValueCountFrequency (%)
035958
90.4%
0.0631
 
< 0.1%
0.0745000011
 
< 0.1%
0.1347999951
 
< 0.1%
0.13931
 
< 0.1%
0.161
 
< 0.1%
0.19521
 
< 0.1%
0.1978999991
 
< 0.1%
0.2007000011
 
< 0.1%
0.21471
 
< 0.1%
ValueCountFrequency (%)
7002.191
< 0.1%
6972.591
< 0.1%
6543.041
< 0.1%
5774.81
< 0.1%
5602.721
< 0.1%
5569.921
< 0.1%
5216.741
< 0.1%
5036.011
< 0.1%
4902.081
< 0.1%
4900.751
< 0.1%

last_pymnt_d
Categorical

HIGH CARDINALITY

Distinct109
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
Mar-13
 
1097
Dec-14
 
945
May-13
 
907
Feb-13
 
869
Apr-13
 
851
Other values (104)
35117 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238716
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowJan-15
2nd rowApr-13
3rd rowJun-14
4th rowJan-15
5th rowJan-17

Common Values

ValueCountFrequency (%)
Mar-131097
 
2.8%
Dec-14945
 
2.4%
May-13907
 
2.3%
Feb-13869
 
2.2%
Apr-13851
 
2.1%
Mar-12844
 
2.1%
Aug-12832
 
2.1%
Aug-14832
 
2.1%
Jan-14832
 
2.1%
Oct-12826
 
2.1%
Other values (99)30951
77.8%

Length

2021-07-27T08:43:13.723841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar-131097
 
2.8%
dec-14945
 
2.4%
may-13907
 
2.3%
feb-13869
 
2.2%
apr-13851
 
2.1%
mar-12844
 
2.1%
aug-14832
 
2.1%
aug-12832
 
2.1%
jan-14832
 
2.1%
oct-12826
 
2.1%
Other values (99)30951
77.8%

Most occurring characters

ValueCountFrequency (%)
144086
18.5%
-39786
16.7%
a10135
 
4.2%
e10093
 
4.2%
u9873
 
4.1%
J9548
 
4.0%
39529
 
4.0%
49269
 
3.9%
28904
 
3.7%
c7129
 
3.0%
Other values (23)80364
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79572
33.3%
Decimal Number79572
33.3%
Uppercase Letter39786
16.7%
Dash Punctuation39786
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a10135
12.7%
e10093
12.7%
u9873
12.4%
c7129
9.0%
r7003
8.8%
p6346
8.0%
n6176
7.8%
t3437
 
4.3%
g3396
 
4.3%
l3372
 
4.2%
Other values (4)12612
15.8%
Decimal Number
ValueCountFrequency (%)
144086
55.4%
39529
 
12.0%
49269
 
11.6%
28904
 
11.2%
02544
 
3.2%
52433
 
3.1%
62084
 
2.6%
9559
 
0.7%
8137
 
0.2%
727
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
J9548
24.0%
M7064
17.8%
A6557
16.5%
D3692
 
9.3%
O3437
 
8.6%
F3216
 
8.1%
S3185
 
8.0%
N3087
 
7.8%
Dash Punctuation
ValueCountFrequency (%)
-39786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119358
50.0%
Common119358
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a10135
 
8.5%
e10093
 
8.5%
u9873
 
8.3%
J9548
 
8.0%
c7129
 
6.0%
M7064
 
5.9%
r7003
 
5.9%
A6557
 
5.5%
p6346
 
5.3%
n6176
 
5.2%
Other values (12)39434
33.0%
Common
ValueCountFrequency (%)
144086
36.9%
-39786
33.3%
39529
 
8.0%
49269
 
7.8%
28904
 
7.5%
02544
 
2.1%
52433
 
2.0%
62084
 
1.7%
9559
 
0.5%
8137
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII238716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
144086
18.5%
-39786
16.7%
a10135
 
4.2%
e10093
 
4.2%
u9873
 
4.1%
J9548
 
4.0%
39529
 
4.0%
49269
 
3.9%
28904
 
3.7%
c7129
 
3.0%
Other values (23)80364
33.7%

last_pymnt_amnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct35241
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2679.092793
Minimum0
Maximum36115.2
Zeros75
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size311.0 KiB
2021-07-27T08:43:13.841147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.9625
Q1218.0725
median549.425
Q33292.1225
95-th percentile12175.15
Maximum36115.2
Range36115.2
Interquartile range (IQR)3074.05

Descriptive statistics

Standard deviation4443.38302
Coefficient of variation (CV)1.658540172
Kurtosis8.886691235
Mean2679.092793
Median Absolute Deviation (MAD)455.41
Skewness2.714116109
Sum106590385.8
Variance19743652.66
MonotonicityNot monotonic
2021-07-27T08:43:13.968531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
075
 
0.2%
20017
 
< 0.1%
5015
 
< 0.1%
10015
 
< 0.1%
40012
 
< 0.1%
15011
 
< 0.1%
275.7411
 
< 0.1%
50010
 
< 0.1%
276.069
 
< 0.1%
2508
 
< 0.1%
Other values (35231)39603
99.5%
ValueCountFrequency (%)
075
0.2%
0.011
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.061
 
< 0.1%
0.131
 
< 0.1%
0.162
 
< 0.1%
0.21
 
< 0.1%
0.221
 
< 0.1%
0.241
 
< 0.1%
ValueCountFrequency (%)
36115.21
< 0.1%
35613.681
< 0.1%
35596.411
< 0.1%
35479.891
< 0.1%
35471.861
< 0.1%
35395.591
< 0.1%
35339.051
< 0.1%
35337.091
< 0.1%
35322.961
< 0.1%
35322.61
< 0.1%

last_credit_pull_d
Categorical

HIGH CARDINALITY

Distinct114
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
Jan-17
10471 
Oct-16
4498 
Dec-16
 
862
Nov-16
 
727
Mar-16
 
693
Other values (109)
22535 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters238716
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowJan-17
2nd rowOct-16
3rd rowJan-17
4th rowApr-16
5th rowJan-17

Common Values

ValueCountFrequency (%)
Jan-1710471
26.3%
Oct-164498
 
11.3%
Dec-16862
 
2.2%
Nov-16727
 
1.8%
Mar-16693
 
1.7%
Feb-13638
 
1.6%
Apr-16546
 
1.4%
Aug-16525
 
1.3%
Feb-16496
 
1.2%
Jul-16482
 
1.2%
Other values (104)19848
49.9%

Length

2021-07-27T08:43:14.250134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jan-1710471
26.3%
oct-164498
 
11.3%
dec-16862
 
2.2%
nov-16727
 
1.8%
mar-16693
 
1.7%
feb-13638
 
1.6%
apr-16546
 
1.4%
aug-16525
 
1.3%
feb-16496
 
1.2%
jul-16482
 
1.2%
Other values (104)19848
49.9%

Most occurring characters

ValueCountFrequency (%)
141307
17.3%
-39786
16.7%
a16351
 
6.8%
J15921
 
6.7%
n13836
 
5.8%
610541
 
4.4%
710506
 
4.4%
c8719
 
3.7%
e6994
 
2.9%
O6101
 
2.6%
Other values (23)68654
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79572
33.3%
Decimal Number79572
33.3%
Uppercase Letter39786
16.7%
Dash Punctuation39786
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a16351
20.5%
n13836
17.4%
c8719
11.0%
e6994
8.8%
t6101
 
7.7%
u6029
 
7.6%
r4348
 
5.5%
p4008
 
5.0%
o2370
 
3.0%
v2370
 
3.0%
Other values (4)8446
10.6%
Decimal Number
ValueCountFrequency (%)
141307
51.9%
610541
 
13.2%
710506
 
13.2%
44949
 
6.2%
54036
 
5.1%
33947
 
5.0%
23122
 
3.9%
0939
 
1.2%
9185
 
0.2%
840
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
J15921
40.0%
O6101
 
15.3%
M4320
 
10.9%
A4080
 
10.3%
D2618
 
6.6%
N2370
 
6.0%
F2309
 
5.8%
S2067
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
-39786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119358
50.0%
Common119358
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a16351
13.7%
J15921
13.3%
n13836
11.6%
c8719
 
7.3%
e6994
 
5.9%
O6101
 
5.1%
t6101
 
5.1%
u6029
 
5.1%
r4348
 
3.6%
M4320
 
3.6%
Other values (12)30638
25.7%
Common
ValueCountFrequency (%)
141307
34.6%
-39786
33.3%
610541
 
8.8%
710506
 
8.8%
44949
 
4.1%
54036
 
3.4%
33947
 
3.3%
23122
 
2.6%
0939
 
0.8%
9185
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII238716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141307
17.3%
-39786
16.7%
a16351
 
6.8%
J15921
 
6.7%
n13836
 
5.8%
610541
 
4.4%
710506
 
4.4%
c8719
 
3.7%
e6994
 
2.9%
O6101
 
2.6%
Other values (23)68654
28.8%

collections_12_mths_ex_med
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
0.0
39786 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters119358
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.039786
100.0%

Length

2021-07-27T08:43:14.458434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:14.526882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.039786
100.0%

Most occurring characters

ValueCountFrequency (%)
079572
66.7%
.39786
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79572
66.7%
Other Punctuation39786
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
079572
100.0%
Other Punctuation
ValueCountFrequency (%)
.39786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common119358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
079572
66.7%
.39786
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII119358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
079572
66.7%
.39786
33.3%

application_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
INDIVIDUAL
39786 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters397860
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
INDIVIDUAL39786
100.0%

Length

2021-07-27T08:43:14.691957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:14.759998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
individual39786
100.0%

Most occurring characters

ValueCountFrequency (%)
I119358
30.0%
D79572
20.0%
N39786
 
10.0%
V39786
 
10.0%
U39786
 
10.0%
A39786
 
10.0%
L39786
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter397860
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I119358
30.0%
D79572
20.0%
N39786
 
10.0%
V39786
 
10.0%
U39786
 
10.0%
A39786
 
10.0%
L39786
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin397860
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I119358
30.0%
D79572
20.0%
N39786
 
10.0%
V39786
 
10.0%
U39786
 
10.0%
A39786
 
10.0%
L39786
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII397860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I119358
30.0%
D79572
20.0%
N39786
 
10.0%
V39786
 
10.0%
U39786
 
10.0%
A39786
 
10.0%
L39786
 
10.0%

chargeoff_within_12_mths
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
0.0
39786 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters119358
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.039786
100.0%

Length

2021-07-27T08:43:14.926988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:14.998111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.039786
100.0%

Most occurring characters

ValueCountFrequency (%)
079572
66.7%
.39786
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79572
66.7%
Other Punctuation39786
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
079572
100.0%
Other Punctuation
ValueCountFrequency (%)
.39786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common119358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
079572
66.7%
.39786
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII119358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
079572
66.7%
.39786
33.3%

delinq_amnt
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
0
39786 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39786
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039786
100.0%

Length

2021-07-27T08:43:15.166126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:15.236044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
039786
100.0%

Most occurring characters

ValueCountFrequency (%)
039786
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39786
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
039786
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039786
100.0%

pub_rec_bankruptcies
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
0.0
37404 
1.0
 
1678
0.04328583488961089
 
697
2.0
 
7

Length

Max length19
Median length3
Mean length3.280299603
Min length3

Characters and Unicode

Total characters130510
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.037404
94.0%
1.01678
 
4.2%
0.04328583488961089697
 
1.8%
2.07
 
< 0.1%

Length

2021-07-27T08:43:15.427259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:15.505927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.037404
94.0%
1.01678
 
4.2%
0.04328583488961089697
 
1.8%
2.07
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
078584
60.2%
.39786
30.5%
83485
 
2.7%
12375
 
1.8%
41394
 
1.1%
31394
 
1.1%
91394
 
1.1%
2704
 
0.5%
5697
 
0.5%
6697
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number90724
69.5%
Other Punctuation39786
30.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
078584
86.6%
83485
 
3.8%
12375
 
2.6%
41394
 
1.5%
31394
 
1.5%
91394
 
1.5%
2704
 
0.8%
5697
 
0.8%
6697
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.39786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common130510
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
078584
60.2%
.39786
30.5%
83485
 
2.7%
12375
 
1.8%
41394
 
1.1%
31394
 
1.1%
91394
 
1.1%
2704
 
0.5%
5697
 
0.5%
6697
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII130510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
078584
60.2%
.39786
30.5%
83485
 
2.7%
12375
 
1.8%
41394
 
1.1%
31394
 
1.1%
91394
 
1.1%
2704
 
0.5%
5697
 
0.5%
6697
 
0.5%

tax_liens
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.0 KiB
0.0
39786 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters119358
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.039786
100.0%

Length

2021-07-27T08:43:15.699661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T08:43:15.770683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.039786
100.0%

Most occurring characters

ValueCountFrequency (%)
079572
66.7%
.39786
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79572
66.7%
Other Punctuation39786
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
079572
100.0%
Other Punctuation
ValueCountFrequency (%)
.39786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common119358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
079572
66.7%
.39786
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII119358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
079572
66.7%
.39786
33.3%

Interactions

2021-07-27T08:41:49.641239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:49.754529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:49.856780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:49.961411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:50.064024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:50.166679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:50.269324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:50.365894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:50.464446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:50.566000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:50.680835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:50.795701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:50.903272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.020838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.127665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.228192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.345046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.462605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.574698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.684322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.793858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.897749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:51.999552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:52.099193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:52.199107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:52.298107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:52.392972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:52.490545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:52.595745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:52.700205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:52.806309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:52.912483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.016032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.116492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.214714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.313181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.420653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.519272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.621525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.720192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.815122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:53.920960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:54.030543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:54.133382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:54.236487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:54.343644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:54.444003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:54.549457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:54.650912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:54.968143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:55.085050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:55.186917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:55.289909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:55.398222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:55.505260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:55.614291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:55.724535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:55.827417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:55.934396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.040483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.142042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.252233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.355386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.463921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.566892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.664794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.774466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.887162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:56.993106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:57.099227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:57.208568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:57.313903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:57.421756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:57.529975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:57.646165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:57.755628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:57.856324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:57.958054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T08:41:58.065533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-07-27T08:43:15.888573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-27T08:43:16.275957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-27T08:43:16.647609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-27T08:43:17.041353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-27T08:43:17.403252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-27T08:43:01.414086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-27T08:43:03.188216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idmember_idloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspymnt_planzip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntlast_credit_pull_dcollections_12_mths_ex_medapplication_typechargeoff_within_12_mthsdelinq_amntpub_rec_bankruptciestax_liens
010775011296599500050004975.036 months10.65162.87BB210+ yearsRENT24000.0VerifiedDec-11Fully Paidn860xxAZ27.650Jan-851301364883.79f0.00.05863.1551875833.845000.00863.160.000.000.00Jan-15171.62Jan-170.0INDIVIDUAL0.000.00.0
110774301314167250025002500.060 months15.2759.83CC4< 1 yearRENT30000.0Source VerifiedDec-11Charged Offn309xxGA1.000Apr-9953016879.44f0.00.01014.5300001014.53456.46435.170.00122.901.11Apr-13119.66Oct-160.0INDIVIDUAL0.000.00.0
210771751313524240024002400.036 months15.9684.33CC510+ yearsRENT12252.0Not VerifiedDec-11Fully Paidn606xxIL8.720Nov-01220295698.510f0.00.03005.6668443005.672400.00605.670.000.000.00Jun-14649.91Jan-170.0INDIVIDUAL0.000.00.0
310768631277178100001000010000.036 months13.49339.31CC110+ yearsRENT49200.0Source VerifiedDec-11Fully Paidn917xxCA20.000Feb-961100559821.037f0.00.012231.89000012231.8910000.002214.9216.970.000.00Jan-15357.48Apr-160.0INDIVIDUAL0.000.00.0
410753581311748300030003000.060 months12.6967.79BB51 yearRENT80000.0Source VerifiedDec-11Fully Paidn972xxOR17.940Jan-9601502778353.938f0.00.04066.9081614066.913000.001066.910.000.000.00Jan-1767.30Jan-170.0INDIVIDUAL0.000.00.0
510752691311441500050005000.036 months7.90156.46AA43 yearsRENT36000.0Source VerifiedDec-11Fully Paidn852xxAZ11.200Nov-04390796328.312f0.00.05632.2100005632.215000.00632.210.000.000.00Jan-15161.03Jan-160.0INDIVIDUAL0.000.00.0
610696391304742700070007000.060 months15.96170.08CC58 yearsRENT47004.0Not VerifiedDec-11Fully Paidn280xxNC23.510Jul-051701772685.611f0.00.010137.84001010137.847000.003137.840.000.000.00May-161313.76Sep-160.0INDIVIDUAL0.000.00.0
710720531288686300030003000.036 months18.64109.43EE19 yearsRENT48000.0Source VerifiedDec-11Fully Paidn900xxCA5.350Jan-07240822187.54f0.00.03939.1352943939.143000.00939.140.000.000.00Jan-15111.34Dec-140.0INDIVIDUAL0.000.00.0
810717951306957560056005600.060 months21.28152.39FF24 yearsOWN40000.0Source VerifiedDec-11Charged Offn958xxCA5.550Apr-042110521032.613f0.00.0647.500000647.50162.02294.940.00190.542.09Apr-12152.39Oct-160.0INDIVIDUAL0.000.00.0
910715701306721537553755350.060 months12.69121.45BB5< 1 yearRENT15000.0VerifiedDec-11Charged Offn774xxTX18.080Sep-04020927936.53f0.00.01484.5900001477.70673.48533.420.00277.692.52Nov-12121.45Dec-160.0INDIVIDUAL0.000.00.0

Last rows

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39778925339252950005000675.036 months11.22164.23CC4< 1 yearOWN80000.0Not VerifiedJul-07Fully Paidn537xxWI1.210Jul-9631512718516.129f0.00.05912.052998798.135000.0912.050.00.00.0Jul-10165.17Jun-070.0INDIVIDUAL0.000.0432860.0
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39780924029239050005000700.036 months8.70158.30BB15 yearsMORTGAGE75000.0Not VerifiedJul-07Fully Paidn804xxCO15.550May-9401006603323.029f0.00.05698.603286797.805000.0698.600.00.00.0Jul-10159.83Nov-140.0INDIVIDUAL0.000.0432860.0
397819218792174250025001075.036 months8.0778.42AA44 yearsMORTGAGE110000.0Not VerifiedJul-07Fully Paidn802xxCO11.330Nov-900130727413.140f0.00.02822.9692931213.882500.0322.970.00.00.0Jul-1080.90Jun-100.0INDIVIDUAL0.000.0432860.0
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39784903768924350005000650.036 months7.43155.38AA2< 1 yearMORTGAGE200000.0Not VerifiedJul-07Fully Paidn208xxMD3.720Nov-880170856070.726f0.00.05174.198551672.665000.0174.200.00.00.0Jan-080.00Jun-070.0INDIVIDUAL0.000.0432860.0
39785870238699975007500800.036 months13.75255.43EE2< 1 yearOWN22000.0Not VerifiedJun-07Fully Paidn027xxMA14.291Oct-03070417551.58f0.00.09195.263334980.837500.01695.260.00.00.0Jun-10256.59Jun-100.0INDIVIDUAL0.000.0432860.0